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Jena, Sudarson
- Accessing Data from Cloud Database using Tunneling
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Authors
Affiliations
1 Department of Computer Science, GITAM University, Hyderabad, Andhra Pradesh, IN
2 Department of Information Technology, GITAM University, Hyderabad, Andhra Pradesh, IN
3 Department of Computer Science, Avanthi’s Scientific Technological and Research Academy, Hyderabad, IN
1 Department of Computer Science, GITAM University, Hyderabad, Andhra Pradesh, IN
2 Department of Information Technology, GITAM University, Hyderabad, Andhra Pradesh, IN
3 Department of Computer Science, Avanthi’s Scientific Technological and Research Academy, Hyderabad, IN
Source
International Journal of Knowledge Based Computer System, Vol 2, No 2 (2014), Pagination: 20-27Abstract
Cloud services are playing an important role in day-today life because of rapid development in information technology. Every IT industry is showing interest to adopt the services of cloud. Cloud provides services on demand of users in cheapest price and without maintenance. The users are very much interested to place their sensitive data in cloud but agitate confidentiality cause of internal and external attacks owing to un-trusted public cloud database. In this paper, providing privacy to sensitive data by adding encrypting layer to data and processing requested queries over encrypted data at un-trusted server by using tunneling at client side has been proposed.Keywords
Cloud Computing, Trusted System, Encryption Mechanisms, DBMS Server, Proxy Server, Tunneling, Client Site.
- Performance Evaluation of Feature Selection Measures
Abstract Views :221 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science and Engineering, GITAM University, Hyderabad, Telangana, IN
2 Department of Information Technology, GITAM University, Hyderabad, Telangana, IN
1 Department of Computer Science and Engineering, GITAM University, Hyderabad, Telangana, IN
2 Department of Information Technology, GITAM University, Hyderabad, Telangana, IN
Source
International Journal of Knowledge Based Computer System, Vol 4, No 2 (2016), Pagination: 29-31Abstract
Feature Selection is one of the approach for solving dimensionality problem. Numerous feature selection filter evaluation measures are used to produce good feature subset. This paper presents the comparison of Information Gain, Correlation and Gain Ratio filter measures to verify the performance of different filter evaluation measures on high dimensional datasets. Computational time required to evaluate dataset with respect to Naïve Bayes classifier is calculated by using filter measures. Experimental results on different datasets demonstrate that Correlation measure is favourable in terms of computational time than other measures.Keywords
Feature Selection, Correlation, Information Gain, Filter Measures, Gain Ratio.References
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